scholarly journals Limited impact of contact tracing in a University setting for COVID-19 due to asymptomatic transmission and social distancing

2021 ◽  
Author(s):  
Daniel A Stocks ◽  
Emily J Nixon ◽  
Adam Trickey ◽  
Martin Homer ◽  
Ellen Brooks-Pollock
Keyword(s):  
Social Contact ◽  
Contact Tracing ◽  
Similar Data ◽  
Social Distancing ◽  
Alternative Interventions ◽  
Multiple Data ◽  
Contact Data ◽  
University Population ◽  
University Setting ◽  
Index Cases

Contact tracing is an important tool for controlling the spread of infectious diseases, including COVID-19. Here, we investigate the spread of COVID-19 and the effectiveness of contact tracing in a university population, using a data-driven ego-centric network model constructed with social contact data collected during 2020 and similar data collected in 2010. We find that during 2020, university staff and students consistently reported fewer social contacts than in 2010, however those contacts occurred more frequently and were of longer duration. We find that contact tracing in the presence of social distancing is less impactful than without social distancing. By combining multiple data sources, we show that University-aged populations are likely to develop asymptomatic COVID-19 infections. We find that asymptomatic index cases cannot be reliably back-traced through contact tracing and consequently transmission in their social network is not significantly reduced through contact tracing. In summary, social distancing restrictions had a large impact on limiting COVID-19 outbreaks in universities; to reduce transmission further contact tracing should be used in conjunction with alternative interventions.

scholarly journals Mapping social distancing measures to the reproduction number for COVID-19

2021 ◽  
Vol 376 (1829) ◽  
pp. 20200276
Author(s):  
Ellen Brooks-Pollock ◽  
Jonathan M. Read ◽  
Angela R. McLean ◽  
Matt J. Keeling ◽  
Leon Danon
Keyword(s):  
Primary Schools ◽  
Reproduction Number ◽  
Age Groups ◽  
Social Contact ◽  
Contact Tracing ◽  
Policy Makers ◽  
Social Distancing ◽  
Trade Offs ◽  
Contact Data ◽  
The Impact

In the absence of a vaccine, severe acute respiratory syndrome–coronavirus 2 (SARS-CoV-2) transmission has been controlled by preventing person-to-person interactions via social distancing measures. In order to re-open parts of society, policy-makers need to consider how combinations of measures will affect transmission and understand the trade-offs between them. We use age-specific social contact data, together with epidemiological data, to quantify the components of the COVID-19 reproduction number. We estimate the impact of social distancing policies on the reproduction number by turning contacts on and off based on context and age. We focus on the impact of re-opening schools against a background of wider social distancing measures. We demonstrate that pre-collected social contact data can be used to provide a time-varying estimate of the reproduction number ( R ). We find that following lockdown (when R = 0.7, 95% CI 0.6, 0.8), opening primary schools has a modest impact on transmission ( R = 0.89, 95% CI 0.82−0.97) as long as other social interactions are not increased. Opening secondary and primary schools is predicted to have a larger impact ( R = 1.22, 95% CI 1.02−1.53). Contact tracing and COVID security can be used to mitigate the impact of increased social mixing to some extent; however, social distancing measures are still required to control transmission. Our approach has been widely used by policy-makers to project the impact of social distancing measures and assess the trade-offs between them. Effective social distancing, contact tracing and COVID security are required if all age groups are to return to school while controlling transmission. This article is part of the theme issue ‘Modelling that shaped the early COVID-19 pandemic response in the UK’.

scholarly journals Combining fine-scale social contact data with epidemic modelling reveals interactions between contact tracing, quarantine, testing and physical distancing for controlling COVID-19

2020 ◽  
Author(s):  
Josh A. Firth ◽  
Joel Hellewell ◽  
Petra Klepac ◽  
Stephen Kissler ◽  
Adam J. Kucharski ◽  
...  
Keyword(s):  
Real World ◽  
Social Dynamics ◽  
Control Strategies ◽  
Single Point ◽  
Local Population ◽  
Social Contact ◽  
Contact Tracing ◽  
Contact Rates ◽  
Contact Data ◽  
Potential Impact

AbstractCase isolation and contact tracing can contribute to the control of COVID-19 outbreaks1,2. However, it remains unclear how real-world networks could influence the effectiveness and efficiency of such approaches. To address this issue, we simulated control strategies for SARS-CoV-2 in a real-world social network generated from high resolution GPS data3,4. We found that tracing contacts-of-contacts reduced the size of simulated outbreaks more than tracing of only contacts, but resulted in almost half of the local population being quarantined at a single point in time. Testing and releasing non-infectious individuals led to increases in outbreak size, suggesting that contact tracing and quarantine may be most effective when it acts as a ‘local lockdown’ when contact rates are high. Finally, we estimated that combining physical distancing with contact tracing could enable epidemic control while reducing the number of quarantined individuals. Our approach highlights the importance of network structure and social dynamics in evaluating the potential impact of SARS-CoV-2 control.

scholarly journals Quantifying social contact patterns in Minnesota during Stay-at-Home social distancing order

2021 ◽  
Author(s):  
Audrey M Dorelien ◽  
Narmada Venkateswaran ◽  
Jiuchen Deng ◽  
Kelly Searle ◽  
Eva Enns ◽  
...  
Keyword(s):  
Negative Binomial ◽  
Social Contact ◽  
Negative Binomial Regression ◽  
Risk Groups ◽  
Social Distancing ◽  
Contact Patterns ◽  
Specific Contact ◽  
Contact Data ◽  
Age Structured ◽  
At Home

SARS-CoV-2 is primarily transmitted through person-to-person contacts. It is important to collect information on age-specific contact patterns because SARS-CoV-2 susceptibility, transmission, and morbidity vary by age. To reduce risk of infection, social distancing measures have been implemented. Social contact data, which identify who has contact with whom especially by age and place are needed to identify high-risk groups and serve to inform the design of non-pharmaceutical interventions. We estimated and used negative binomial regression to compare the number of daily contacts during the first wave (April-May 2020) of the Minnesota Social Contact Study, based on respondents age, gender, race/ethnicity, region, and other demographic characteristics. We used information on age and location of contacts to generate age-structured contact matrices. Finally, we compared the age-structured contact matrices during the stay-at-home order to pre-pandemic matrices. During the state-wide stay-home order, the mean daily number of contacts was 5.6. We found significant variation in contacts by age, gender, race, and region. Adults between 40 and 50 years had the highest number of contacts. Respondents in Black households had 2.1 more contacts than respondent in White households, while respondents in Asian or Pacific Islander households had approximately the same number of contacts as respondent in White households. Respondents in Hispanic households had approximately two fewer contacts compared to White households. Most contacts were with other individuals in the same age group. Compared to the pre-pandemic period, the biggest declines occurred in contacts between children, and contacts between those over 60 with those below 60.

scholarly journals A data-driven metapopulation model for the Belgian COVID-19 epidemic: assessing the impact of lockdown and exit strategies

2020 ◽  
Author(s):  
Pietro Coletti ◽  
Pieter Libin ◽  
Oana Petrof ◽  
Lander Willem ◽  
Abrams Steven ◽  
...  
Keyword(s):  
Phase 1 ◽  
Leisure Activities ◽  
National Level ◽  
Social Contact ◽  
Contact Tracing ◽  
Exit Strategy ◽  
Social Distancing ◽  
Initial Wave ◽  
The Impact ◽  
Second Wave

In response to the ongoing COVID-19 pandemic, several countries adopted measures of social distancing to a different degree. For many countries, after successfully curbing the initial wave, lockdown measures were gradually lifted. In Belgium, such relief started on May 4th with phase 1, followed by several subsequent phases over the next few weeks. We analysed the expected impact of relaxing stringent lockdown measures taken according to the phased Belgian exit strategy. We developed a stochastic, data-informed, meta-population model that accounts for mixing and mobility of the age-structured population of Belgium. The model is calibrated to daily hospitalization data and serological data and is able to reproduce the outbreak at the national level. We consider different scenarios for relieving the lockdown, quantified in terms of relative reductions in pre-pandemic social mixing and mobility. We validate our assumptions by making comparisons with social contact data collected during and after the lockdown. Our model is able to successfully describe the initial wave of COVID-19 in Belgium and identifies interactions during leisure/other activities as pivotal in the exit strategy. Indeed, we find a smaller impact of school re-openings as compared to restarting leisure activities and re-openings of work places. We also assess the impact of case isolation of new (suspected) infections, and find that it allows re-establishing relatively more social interactions while still ensuring epidemic control. Scenarios predicting a second wave of hospitalizations were not observed, suggesting that the per-contact probability of infection has changed with respect to the pre-lockdown period. Community contacts are found to be most influential, followed by professional contacts and school contacts, respectively, for an impending second wave of COVID-19. Regular re-assessment is crucial to adjust to evolving behavioral changes that can affect epidemic diffusion. In addition to social distancing, sufficient capacity for extensive testing and contact tracing is essential for successful mitigation.

scholarly journals Using social contact data to predict and compare the impact of social distancing policies with implications for school re-opening

2020 ◽  
Author(s):  
Ellen Brooks-Pollock ◽  
Jonathan M Read ◽  
Angela R McLean ◽  
Matt J Keeling ◽  
Leon Danon
Keyword(s):  
Primary School ◽  
Primary Schools ◽  
Reproduction Number ◽  
Social Contact ◽  
Return To School ◽  
Social Distancing ◽  
Community Mobility ◽  
Contact Data ◽  
The Uk ◽  
The Impact

Background Social distancing measures, including school closures, are being used to control SARS-CoV-2 transmission in many countries. Once "lockdown" has driven incidence to low levels, selected activities are being permitted. Re-opening schools is a priority because of the welfare and educational impact of closures on children. However, the impact of school re-opening needs to be considered within the context of other measures. Methods We use social contact data from the UK to predict the impact of social distancing policies on the reproduction number. We calibrate our tool to the COVID-19 epidemic in the UK using publicly available death data and Google Community Mobility Reports. We focus on the impact of re-opening schools against a back-drop of wider social distancing easing. Results We demonstrate that pre-collected social contact data, combined with incidence data and Google Community Mobility Reports, is able to provide a time-varying estimate of the reproduction number (R). From an pre-control setting when R=2.7 (95%CI 2.5, 2.9), we estimate that the minimum reproduction number that can be achieved in the UK without limiting household contacts is 0.45 (95%CI:0.41-0.50); in the absence of other changes, preventing leisure contacts has a smaller impact (R=2.0,95%CI:1.8-2.4) than preventing work contacts (R=1.5,95%CI:1.4-1.7). We find that following lockdown (when R=0.7 (95% CI 0.6, 0.8)), opening primary schools in isolation has a modest impact on transmission R=0.83 (95%CI:0.77-0.90) but that high adherence to other measures is needed. Opening secondary schools as well as primary school is predicted to have a larger overall impact (R=0.95,95%CI:0.85-1.07), however transmission could still be controlled with effective contact tracing. Conclusions Our findings suggest that primary school children can return to school without compromising transmission, however other measures, such as social distancing and contract tracing, are required to control transmission if all age groups are to return to school. Our tool provides a mapping from policies to the reproduction number and can be used by policymakers to compare the impact of social-easing measures, dissect mitigation strategies and support careful localized control strategies.

Are social networking platforms the key to effective social distancing for COVID-19? (Preprint)

2020 ◽  
Author(s):  
Viknesh Sounderajah ◽  
Hutan Ashrafian ◽  
Sheraz Markar ◽  
Ara Darzi
Keyword(s):  
Operating System ◽  
Social Networking ◽  
Contact Tracing ◽  
Text Data ◽  
Social Distancing ◽  
Web Mapping ◽  
Viral Spread ◽  
Behavioural Insights ◽  
Mapping Software ◽  
Incidence And Mortality

UNSTRUCTURED If health systems are to effectively employ social distancing measures to in response to further COVID-19 peaks, they must adopt new behavioural metrics that can supplement traditional downstream measures, such as incidence and mortality. Access to mobile digital innovations may dynamically quantify compliance to social distancing (e.g. web mapping software) as well as establish personalised real-time contact tracing of viral spread (e.g. mobile operating system infrastructure through Google-Apple partnership). In particular, text data from social networking platforms can be mined for unique behavioural insights, such as symptom tracking and perception monitoring. Platforms, such as Twitter, have shown significant promise in tracking communicable pandemics. As such, it is critical that social networking companies collaborate with each other in order to (1) enrich the data that is available for analysis, (2) promote the creation of open access datasets for researchers and (3) cultivate relationships with governments in order to affect positive change.

scholarly journals COVID Lockdowns, Social Distancing, and Fatal Car Crashes: More Deaths on Hobbesian Highways?

2020 ◽  
Vol 4 (3-4) ◽  
pp. 238-259 ◽  
Author(s):  
Marshall W. Meyer
Keyword(s):  
World War Ii ◽  
Traffic Safety ◽  
Motor Vehicle ◽  
Road Traffic ◽  
Research Question ◽  
Traffic Volume ◽  
Similar Data ◽  
World War ◽  
Social Distancing ◽  
The Usa

Abstract Research Question What happened to US traffic safety during the first US COVID-19 lockdown, and why was the pattern the opposite of that observed in previous sudden declines of traffic volume? Data National and local statistics on US traffic volume, traffic fatalities, injury accidents, speeding violations, running of stop signs, and other indicators of vehicular driving behavior, both in 2020 and in previous US economic recessions affecting the volume of road traffic. Methods Comparative analysis of the similarities and differences between the data for the COVID-19 lockdown in parts of the USA in March 2020 and similar data for the 2008–2009 global economic crisis, as well as other US cases of major reductions in traffic volume. Findings The volume of traffic contracted sharply once a COVID-19 national emergency was declared and most states issued stay-at-home orders, but motor vehicle fatality rates, injury accidents, and speeding violations went up, and remained elevated even as traffic began returning toward normal. This pattern does not fit post-World War II recessions where fatality rates declined with the volume of traffic nor does the 2020 pattern match the pattern during World War II when traffic dropped substantially with little change in motor vehicle fatality rates. Conclusions The findings are consistent with a theory of social distancing on highways undermining compliance with social norms, a social cost of COVID which, if not corrected, poses potential long-term increases in non-compliance and dangerous driving.

scholarly journals Investigation of Four Clusters of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) in Rwanda, 2020

2021 ◽  
Vol 18 (13) ◽  
pp. 7018
Author(s):  
Olivier Nsekuye ◽  
Edson Rwagasore ◽  
Marie Aime Muhimpundu ◽  
Ziad El-Khatib ◽  
Daniel Ntabanganyimana ◽  
...  
Keyword(s):  
High Risk ◽  
Severe Acute Respiratory Syndrome ◽  
Symptom Onset ◽  
Attack Rate ◽  
Positive Sample ◽  
Contact Tracing ◽  
Face To Face ◽  
Laboratory Confirmation ◽  
Closed Environment ◽  
Index Cases

We reported the findings of the first Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) four clusters identified in Rwanda. Case-investigations included contact elicitation, testing, and isolation/quarantine of confirmed cases. Socio-demographic and clinical data on cases and contacts were collected. A confirmed case was a person with laboratory confirmation of SARS-CoV-2 infection (PCR) while a contact was any person who had contact with a SARS-CoV-2 confirmed case within 72 h prior, to 14 days after symptom onset; or 14 days before collection of the laboratory-positive sample for asymptomatic cases. High risk contacts were those who had come into unprotected face-to-face contact or had been in a closed environment with a SARS-CoV-2 case for >15 min. Forty cases were reported from four clusters by 22 April 2020, accounting for 61% of locally transmitted cases within six weeks. Clusters A, B, C and D were associated with two nightclubs, one house party, and different families or households living in the same compound (multi-family dwelling). Thirty-six of the 1035 contacts tested were positive (secondary attack rate: 3.5%). Positivity rates were highest among the high-risk contacts compared to low-risk contacts (10% vs. 2.2%). Index cases in three of the clusters were imported through international travelling. Fifteen of the 40 cases (38%) were asymptomatic while 13/25 (52%) and 8/25 (32%) of symptomatic cases had a cough and fever respectively. Gatherings in closed spaces were the main early drivers of transmission. Systematic case-investigations contact tracing and testing likely contributed to the early containment of SARS-CoV-2 in Rwanda.

scholarly journals A data-driven metapopulation model for the Belgian COVID-19 epidemic: assessing the impact of lockdown and exit strategies

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Pietro Coletti ◽  
Pieter Libin ◽  
Oana Petrof ◽  
Lander Willem ◽  
Steven Abrams ◽  
...  
Keyword(s):  
Phase 1 ◽  
Leisure Activities ◽  
National Level ◽  
Social Contact ◽  
Exit Strategy ◽  
Initial Wave ◽  
Contact Data ◽  
Stochastic Data ◽  
The Impact ◽  
Second Wave

Abstract Background In response to the ongoing COVID-19 pandemic, several countries adopted measures of social distancing to a different degree. For many countries, after successfully curbing the initial wave, lockdown measures were gradually lifted. In Belgium, such relief started on May 4th with phase 1, followed by several subsequent phases over the next few weeks. Methods We analysed the expected impact of relaxing stringent lockdown measures taken according to the phased Belgian exit strategy. We developed a stochastic, data-informed, meta-population model that accounts for mixing and mobility of the age-structured population of Belgium. The model is calibrated to daily hospitalization data and is able to reproduce the outbreak at the national level. We consider different scenarios for relieving the lockdown, quantified in terms of relative reductions in pre-pandemic social mixing and mobility. We validate our assumptions by making comparisons with social contact data collected during and after the lockdown. Results Our model is able to successfully describe the initial wave of COVID-19 in Belgium and identifies interactions during leisure/other activities as pivotal in the exit strategy. Indeed, we find a smaller impact of school re-openings as compared to restarting leisure activities and re-openings of work places. We also assess the impact of case isolation of new (suspected) infections, and find that it allows re-establishing relatively more social interactions while still ensuring epidemic control. Scenarios predicting a second wave of hospitalizations were not observed, suggesting that the per-contact probability of infection has changed with respect to the pre-lockdown period. Conclusions Contacts during leisure activities are found to be most influential, followed by professional contacts and school contacts, respectively, for an impending second wave of COVID-19. Regular re-assessment of social contacts in the population is therefore crucial to adjust to evolving behavioral changes that can affect epidemic diffusion.

scholarly journals Digital proximity tracing on empirical contact networks for pandemic control

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
G. Cencetti ◽  
G. Santin ◽  
A. Longa ◽  
E. Pigani ◽  
A. Barrat ◽  
...  
Keyword(s):  
Large Scale ◽  
Disease Outbreaks ◽  
Physical Distance ◽  
Contact Tracing ◽  
Reproductive Number ◽  
Modeling Framework ◽  
Infectious Disease Outbreaks ◽  
Contact Patterns ◽  
Contact Data ◽  
The Impact

AbstractDigital contact tracing is a relevant tool to control infectious disease outbreaks, including the COVID-19 epidemic. Early work evaluating digital contact tracing omitted important features and heterogeneities of real-world contact patterns influencing contagion dynamics. We fill this gap with a modeling framework informed by empirical high-resolution contact data to analyze the impact of digital contact tracing in the COVID-19 pandemic. We investigate how well contact tracing apps, coupled with the quarantine of identified contacts, can mitigate the spread in real environments. We find that restrictive policies are more effective in containing the epidemic but come at the cost of unnecessary large-scale quarantines. Policy evaluation through their efficiency and cost results in optimized solutions which only consider contacts longer than 15–20 minutes and closer than 2–3 meters to be at risk. Our results show that isolation and tracing can help control re-emerging outbreaks when some conditions are met: (i) a reduction of the reproductive number through masks and physical distance; (ii) a low-delay isolation of infected individuals; (iii) a high compliance. Finally, we observe the inefficacy of a less privacy-preserving tracing involving second order contacts. Our results may inform digital contact tracing efforts currently being implemented across several countries worldwide.

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